Revealing the driving effect of emissions and meteorology on PM2.5 and O3 trends through a new algorithmic model.

Quantifying the driving effect of each factor on atmospheric secondary pollutants is crucial for pollution prevention. We aim to establish a simple and accessible method to identify ozone (O3) and particulate matter (PM2.5) concentration trends induced by emissions and meteorology. The method comprises five main steps, which involve matrix construction and mutual calculations, and the whole process is demonstrated and verified by employing long-term monitoring data. With regard to the case study, O3 and PM2.5 concentration variance between the target and base year are respectively -4.74 and 0.20 µg/m3 under same meteorological conditions, among which the contribution of the emissions driver and meteorological driver are respectively -5.81 and 1.07 µg/m3 for O3 and respectively 0.55 and -0.35 µg/m3 for PM2.5. Additionally, 84.45% of O3 variance is attributable to the emissions driver in terms of relative importance, which is 52.88% for PM2.5. The meteorological driver is further separated into atmospheric secondary reaction and regional transport. The results reveal that ongoing prevention policy for O3 is effective; however, it needs to be further optimized for PM2.5.

A new classification approach to enhance future VOCs emission policies: Taking solvent-consuming industry as an example.

Volatile organic compounds (VOCs) has consistently been linked to ozone (O3) and secondary organic aerosol (SOA) formation, and ongoing emission policies are primarily focusing on total VOCs without addressing the association between regulation measures and secondary pollution characteristic. For enhancing VOCs emission policy, we investigated potential formation of O3 and SOA based on analyses of node-specific VOCs concentration and species distribution in solvent-consuming industry. Although aromatics were found to contribute most to O3 and SOA formation averagely (2.57 ± 2.14 g O3/g VOCs, 1.91 ± 1.67 g SOA/g VOCs), however, large disparity concerning emission and secondary pollution profile were identified among different emission nodes which demonstrated that regulation policy should be formulated based on comprehensive pollution characteristic. Therefore, emission nodes were classified into four clusters through data normalization, formatting and classification process, including aromatics dominated (7 emission nodes), aromatics-alkene dominated (4 emission nodes), aromatics-alcohols dominated (4 emission nodes) and alcohols dominated (4 emission nodes). And different dominating VOCs species were further obtained in each cluster. Subsequently, focusing regulation measures of reducing O3 and SOA for different emission source clusters were proposed to guide pollution prevention and enhance future VOCs emission policies.

[Pollution Characteristics of Volatile Organic Compounds Emission from the Metal Packaging Industry Based on Analysis of Process].

This study identified the generation and emission nodes of volatile organic compounds (VOCs) in the metal packaging industry, analyzed the VOCs concentration and species from different production processes, and accounted for secondary pollution through the maximum incremental reactivity method and modified fractional aerosol coefficient method. The results indicated that the main VOCs species were benzenes, alcohols, ketones, and esters, and the benzenes and alcohols contributed more in different types of processes and emission nodes, whereas the ketones and esters contributed less. The maximum concentration was 269.08mg·m-3 (n-butanol). Strong correlation was found between the concentrations of the production line and their corresponding exhaust, but the VOC species were not totally identical. Furthermore, the potential formations of ozone and secondary organic aerosols were (3.09±0.94) g·g-1 and (2.58±1.99) g·g-1, respectively, expressed by O3/VOCs and SOA/VOCs, and the benzenes and internal coating drying being the major precursors and emission node.

Algorithm developed for dynamic quantification of coal consumption for and emission from rural winter heating.

Coal-dominated winter heating practices in China are largely accepted to be a leading cause of winter haze in the region though the amount of coal for heating is actually much lower than for power generation or industrial process. However, little is known about how the total rural coal weight in a region could be attributed to real time (e.g., daily) patterns, limiting the understanding of dynamic impacts of coal emissions and the adoption of timely measures against predicted haze. Considering that winter heating essentially protects against cold temperatures, coal burning strength may be related to the temperatures that people experience. A field study was organized to test the validity of this hypothesis. A system was designed to continuously monitor every instance of coal addition, and coal consumption on any given day for a whole village (WDAY) was calculated by summating all the additions. Meanwhile, a new term, composite temperature (TCOM), which incorporates a few weather-related elements, was introduced to represent cold temperatures that individuals experience. It was found that WDAY and TCOM presented opposite variations, and a negative linear correlation was observed (WDAY = -0.75TCOM + 11.86, R2 = 0.75), revealing the feasibility of estimating coal consumption on a certain day (WDAY) based on weather data (TCOM) for a given village. An extensive form of the algorithm for any area of interest (e.g., a district, city, or province) can be expressed as WDAY = (-0.75TCOM + 11.86)‧NH/834, where NH denotes the number of households in a region. This algorithm reflects the essence of winter heating (to resist cold temperatures), and therefore its logic is highly likely to be useful for any countries of the world regardless of what forms of energy used (coal or other energy forms) provided the energy involved is unexceptionally used for winter heating, though there may be some uncertainties in estimated coal consumption due to multiple factors.

Village energy survey reveals missing rural raw coal in northern China: Significance in science and policy.

Burning coal for winter heating has been considered a major contributor to northern China's winter haze, with the district heating boilers holding the balance. However a decade of intensive efforts on district heating boilers brought few improvements to northern China's winter air quality, arousing a speculation that the household heating stoves mainly in rural area rather than the district heating boilers mainly in urban area dominate coal emissions in winter. This implies an extreme underestimation of rural household coal consumption by the China Energy Statistical Yearbooks (CESYs), although direct evidence supporting this speculation is lacking. A village energy survey campaign was launched to gather the firsthand information on household coal consumption in the rural areas of two cities, Baoding (in Hebei province) and Beijing (the capital of China). The survey data show that the rural raw coal consumption in Baoding (5.04 × 103 kt) was approximately 6.5 times the value listed in the official CESY 2013 and exceeded the rural total of whole Hebei Province (4668 kt), revealing a huge amount of raw coal missing from the current statistical system. More importantly, rural emissions of particulate matter (PM) and SO2 from raw coal, which had never been included in widely distributing environmental statistical reports, were found higher than those from industrial and urban household sectors in the two cities in 2013, which highlights the importance of rural coal burning in creating northern China's heavy haze and helps to explain why a number of modeling predictions on ambient pollutant concentrations based on normal emission inventories were more bias-prone in winter season than in other seasons. We therefore recommend placing greater emphasis on the "missing" rural raw coal to help China in its long-term ambition to achieve clean air in the context of rapid economic development.

Air pollutant emission from the underestimated households' coal consumption source in China.

In order to improve the regional air quality, many control strategies have been developed by Chinese government for reducing air pollutant emission from power plants, industrial and transport sources during the past decade. However, little attention has been paid to residential combustion sources. To fill the knowledge gap, a series of surveys were carried out to investigate the residential energy use in Beijing-Tianjin-Hebei (BTH) region during the period of 2013-2014. Study shows that the actual average amount of residential coal consumption is over 0.7tyr-1 per capita in 2013, which is much higher than that of 0.15tyr-1 per capita reported in the 2014 China Energy Statistical Yearbook (CESY). Combining the investigated activities data with the best available emission factors (EFs), bottom-up method was used to evaluate the potential air pollutant emissions from residential coal combustion in BTH region in 2013. The results indicate that Baoding is the top contributor to the whole BTH region and accounts for approximately 15% of the regional residential emissions in 2013. The spatial pattern of air pollutants shows that high emissions locate in the southeast, along the Yanshan and Taihang Mountains, where much more rural people live and coal combustion is prevalent in winter. The future emission scenario at the end of the 13th Five Year Plan (in 2020) was also predicted based on the policy guidance for the residential coal consumptions in the BTH region. The scenario analysis indicates that air pollutant emissions will drop substantially around 90% because more strict rules will be made for reducing the residential coal consumption. With combined survey information and statistical data, the uncertainty of the emission inventory which was established in this study for the residential sector in the BTH region is reduced and the emission inventory is more reliable for air quality decision making.